基于差分进化和粒子群优化算法的混合优化算法

Hybrid optimization algorithm based on differential evolution and particle swarm optimization

为了发挥差分进化和粒子群优化算法各自拥有的特点,并克服自身存在的问题,提出了一种混合优化算法(简称DPA)。该算法首先利用差分进化的变异和选择算子产生新的群体,然后通过使用粒子群优化算法和交叉、选择算子进行局部搜索。在整个算法过程中,群体寻优范围先扩散再收缩,反复迭代渐进收敛。通过3个标准算例的测试表明,新的混合优化算法与差分进化和粒子群优化算法相比,具有收敛速度快、搜索能力强、鲁棒性好的特点。

To take advantage of different algorithms and overcome their limitations, a hybrid optimization algorithm （DPA） is proposed, based on the combination of differential evolution （DE） and particle swarm optimization （PSO）. In the first step of DPA, differential mutation and selection operators are employed to produce a new population for effective variation. Next, PSO is carried out for local exploration with high efficiency, followed by crossover and selection operations. Thus, the extent of search region for the population is increased and decreased sequently at each iteration of the DPA progress, and eventually resulted in convergence to an optimal solution. Numerical tests on four benchmark functions are conducted for the algrithom evaluation. The results show the improvement in the convergence speed, searching ability, and computation stability by comparison with the DE and PSO.